Event Study Design for Modeling Early Relaxation in Turkish Public with COVID-19 Vaccine.

OBJECTIVE: Vaccination is crucial to fighting the coronavirus disease (COVID-19) pandemic. A large body of literature investigates the effect of the initiation of the COVID-19 vaccination in case numbers in Turkey, including the resistance and willingness to taking the vaccine. The effect of early relaxation in the Turkish public with the initiation of vaccination on new daily cases is unknown. METHODS: This study performs an event study analysis to explore the pre-relaxation effect of vaccination on the Turkish public by using daily data of new cases, stringency index, and residential mobility. Two events are comparatively defined as the vaccination of the health personnel (Event 1) and the citizens age 65 and over (Event 2). The initial dates of these events are January 13 and February 12, 2021, respectively. The length of the estimation window is determined as 14 days for the 2 events. To represent only the early stages of the vaccination, the study period ends on April 12, 2021. Thus, whereas the event window of Event 1 includes 90 observations, Event 2 covers 60 observations. RESULTS: While average values of residential mobility, stringency index, and daily numbers of cases are 15.36, 71.03, and 11 978.93 in the estimation window for Event 1, these averages are 8.89, 70.88, and 17 303.20 in the event window. For Event 2, the same average values are 9.14, 69.38, and 7 664.93 in the estimation window and 8.25, 71.12, and 22 319.10 in the event window. When 14-day abnormal growth rates of the daily number of cases for Event 1 and Event 2 are compared, it is observed that Event 1 has negative growth rates initially and reaches a 7.59% growth at most. On the other hand, Event 2 starts with a 1.11% growth rate, and having a steady increase, it reaches a 23.70% growth in the last 14 days of the study period. CONCLUSION: The preliminary result shows that, despite taking more strict governmental measures, while residential mobility decreases, the daily number of COVID-19 cases increases in the early stages of vaccination compared to short pre-periods of it. This indicates that the initiation of vaccination leads to early behavioral relaxation in public. Moreover, the effect of Event 2 on the case numbers is more significant and immediate, compared to that of Event 1, which may be linked to the characteristic of the Turkish culture being more sensitive to the older adult population.

Factors Relating to Decision Delay in the Emergency Department: Effects of Diagnostic Tests and Consultations.

Purpose: The purpose of this study is to investigate the factors increasing waiting time (WT) and length of stay (LOS) in patients, which may cause delays in decision-making in the emergency departments (ED). Patients and Methods: Patients who arrived at a training hospital in the central region of Izmir City, Turkey, during the first quarter of 2020 were retrospectively analyzed. WT and LOS were the outcome variables of the study, and gender, age, arrival type, triage level determined based on the clinical acuity, diagnosis encoded based on International Classification of Diseases-10 (ICD-10), the existence of diagnostic tests or consultation status were the identified factors. The significance of the differences in WT and LOS values based on each level of these factors was analyzed using independent sample t-tests and ANOVA. Results: While patients for which no diagnostic testing or consultation was requested had a significantly higher WT in EDs, their LOS values were substantially lower than those for which at least one diagnostic test or consultation was ordered (p≤0.001). Besides, elderly and red zone patients and those who arrived by ambulance had significantly lower WT and higher LOS values than other levels for all groups of patients for which laboratory-type or imaging-type diagnostic test or consultation was requested (p≤0.001 for each comparison). Conclusion: Besides ordering diagnostic tests or consultation in EDs, different factors may extend patients' WT and LOS values and cause significant decision-making delays. Understanding the patient characteristics associated with longer waiting times and LOS values and, thus, delayed decisions will enable practitioners to improve operations management in EDs.

Fiscal responses to COVID-19 outbreak for healthy economies: Modelling with big data analytics.

Fiscal responses to the COVID-19 crisis have varied a lot across countries. Using a panel of 127 countries over two separate subperiods between 2020 and 2021, this paper seeks to determine the extent that fiscal responses contributed to the spread and containment of the disease. The study first documents that rich countries, which had the largest total and health-related fiscal responses, achieved the lowest fatality rates, defined as the ratio of COVID-related deaths to cases, despite having the largest recorded numbers of cases and fatalities. The next most successful were less developed economies, whose smaller total fiscal responses included a larger health-related component than emerging market economies. The study used a promising big data analytics technology, the random forest algorithm, to determine which factors explained a country's fatality rate. The findings indicate that a country's fatality ratio over the next period can be almost entirely predicted by its economic development level, fiscal expenditure (both total and health-related), and initial fatality ratio. Finally, the study conducted a counterfactual exercise to show that, had less developed economies implemented the same fiscal responses as the rich (as a share of GDP), then their fatality ratios would have declined by 20.47% over the first period and 2.59% over the second one.

Big data analytics and the effects of government restrictions and prohibitions in the COVID-19 pandemic on emergency department sustainable operations.

Grounded in dynamic capabilities, this study mainly aims to model emergency departments' (EDs) sustainable operations in the current situation caused by the COVID-19 pandemic by using emerging big data analytics (BDA) technologies. Since government may impose some restrictions and prohibitions in coping with emergencies to protect the functioning of EDs, it also aims to investigate how such policies affect ED operations. The proposed model is designed by collecting big data from multiple sources and implementing BDA to transform it into action for providing efficient responses to emergencies. The model is validated in modeling the daily number of patients, the average daily length of stay (LOS), and daily numbers of laboratory tests and radiologic imaging tests ordered. It is applied in a case study representing a large-scale ED. The data set covers a seven-month period which collectively means the periods before COVID-19 and during COVID-19, and includes data from 238,152 patients. Comparing statistics on daily patient volumes, average LOS, and resource usage, both before and during the COVID-19 pandemic, we found that patient characteristics and demographics changed in COVID-19. While 18.92% and 27.22% of the patients required laboratory and radiologic imaging tests before-COVID-19 study period, these percentages were increased to 31.52% and 39.46% during-COVID-19 study period. By analyzing the effects of policy-based variables in the model, we concluded that policies might cause sharp decreases in patient volumes. While the total number of patients arriving before-COVID-19 was 158,347, it decreased to 79,805 during-COVID-19. On the other hand, while the average daily LOS was 117.53 min before-COVID-19, this value was calculated to be 165,03 min during-COVID-19 study period. We finally showed that the model had a prediction accuracy of between 80 to 95%. While proposing an efficient model for sustainable operations management in EDs for dynamically changing environments caused by emergencies, it empirically investigates the impact of different policies on ED operations.

The likelihood of requiring a diagnostic test: Classifying emergency department patients with logistic regression.

BACKGROUND: Emergency departments (EDs) play an important role in health systems since they are the front line for patients with emergency medical conditions who frequently require diagnostic tests and timely treatment. OBJECTIVE: To improve decision-making and accelerate processes in EDs, this study proposes predictive models for classifying patients according to whether or not they are likely to require a diagnostic test based on referral diagnosis, age, gender, triage category and type of arrival. METHOD: Retrospective data were categorised into four output patient groups: not requiring any diagnostic test (group A); requiring a radiology test (group B); requiring a laboratory test (group C); requiring both tests (group D). Multivariable logistic regression models were used, with the outcome classifications represented as a series of binary variables: test (1) or no test (0); in the case of group A, no test (1) or test (0). RESULTS: For all models, age, triage category, type of arrival and referral diagnosis were significant predictors whereas gender was not. The main referral diagnosis with high model coefficients varied by designed output groups (groups A, B, C and D). The overall accuracies of the logistic regression models for groups A, B, C and D were, respectively, 74.11%, 73.07%, 82.47% and 85.79%. Specificity metrics were higher than the sensitivities for groups B, C and D, meaning that these models were better able to predict negative outcomes. IMPLICATIONS: These results provide guidance for ED triage staff, researchers and practitioners in making rapid decisions regarding patients' diagnostic test requirements based on specified variables in the predictive models. This is critical in ED operations planning as it potentially decreases waiting times, while increasing patient satisfaction and operational performance.

Big data analytics and COVID-19: investigating the relationship between government policies and cases in Poland, Turkey and South Korea.

We used big data analytics for exploring the relationship between government response policies, human mobility trends and numbers of coronavirus disease 2019 (COVID-19) cases comparatively in Poland, Turkey and South Korea. We collected daily mobility data of retail and recreation, grocery and pharmacy, parks, transit stations, workplaces, and residential areas. For quantifying the actions taken by governments and making a fairness comparison between these countries, we used stringency index values measured with the 'Oxford COVID-19 government response tracker'. For the Turkey case, we also developed a model by implementing the multilayer perceptron algorithm for predicting numbers of cases based on the mobility data. We finally created scenarios based on the descriptive statistics of the mobility data of these countries and generated predictions on the numbers of cases by using the developed model. Based on the descriptive analysis, we pointed out that while Poland and Turkey had relatively closer values and distributions on the study variables, South Korea had more stable data compared to Poland and Turkey. We mainly showed that while the stringency index of the current day was associated with mobility data of the same day, the current day's mobility was associated with the numbers of cases 1 month later. By obtaining 89.3% prediction accuracy, we also concluded that the use of mobility data and implementation of big data analytics technique may enable decision-making in managing uncertain environments created by outbreak situations. We finally proposed implications for policymakers for deciding on the targeted levels of mobility to maintain numbers of cases in a manageable range based on the results of created scenarios.

How machine learning facilitates decision making in emergency departments: Modelling diagnostic test orders.

OBJECTIVES: Since emergency departments (EDs) are responsible for providing initial care for patients who may need urgent medical care, they are highly sensitive to increased patient delays. A key factor that increases patient delays is ordering diagnostic tests. Therefore, understanding the factors increasing diagnostic test orders and proposing efficient models may facilitate decision making in EDs. METHODS: Month and week of the year, day of the week, and daily numbers of patients encoded based on 21 different ICD-10 codes were used as input variables. Daily test frequencies of patients requiring tests from laboratory and imaging services were modelled separately by linear regression models. Although significance of the input variables was identified based on these models, obtained forecasts and residuals were further processed by machine learning techniques to obtain hybrid models. RESULTS: Day of the week, and number of patients with ICD-10 codes of 'A00-B99', 'I00-I99', 'J00-J99', 'M00-M99' and 'R00-R99' were significant in both test types. In addition to these, although daily patient frequencies with 'H60-H95', 'N00-N99' and 'O00-O9A' were significant for laboratory services, 'L00-L99', 'S00-T88' and 'Z00-Z99' were significant for imaging services. Although prediction accuracies of regression models were, respectively, as 93.658% and 95.028% for laboratory and imaging services modelling, they increased to 99.997% and 99.995% with the machine learning-integrated hybrid model. CONCLUSION: The significant factors identified here can predict increases in use of laboratory and imaging services. This could enable these services to be prepared in advance to reduce ED patient delays, thereby reducing ED overcrowding. The proposed model may also be efficiently used for decision making.

Predicting waiting and treatment times in emergency departments using ordinal logistic regression models.

BACKGROUND: Since providing timely care is the primary concern of emergency departments (EDs), long waiting times increase patient dissatisfaction and adverse outcomes. Especially in overcrowded ED environments, emergency care quality can be significantly improved by developing predictive models of patients' waiting and treatment times to use in ED operations planning. METHODS: Retrospective data on 37,711 patients arriving at the ED of a large urban hospital were examined. Ordinal logistic regression models were proposed to identify factors causing increased waiting and treatment times and classify patients with longer waiting and treatment times. RESULTS: According to the proposed ordinal logistic regression model for waiting time prediction, age, arrival mode, and ICD-10 encoded diagnoses are all significant predictors. The model had 52.247% accuracy. The model for treatment time showed that in addition to age, arrival mode, and diagnosis, triage level was also a significant predictor. The model had 66.365% accuracy. The model coefficients had negative signs in the corresponding models, indicating that waiting times are negatively related to treatment times. CONCLUSION: By predicting patients' waiting and treatment times, ED workloads can be assessed instantly. This enables ED personnel to be scheduled to better manage demand supply deficiencies, increase patient satisfaction by informing patients and relatives about expected waiting times, and evaluate performances to improve ED operations and emergency care quality.

Analyzing Main and Interaction Effects of Length of Stay Determinants in Emergency Departments.

BACKGROUND: Measuring and understanding main determinants of length of stay (LOS) in emergency departments (EDs) is critical from an operations perspective, since LOS is one of the main performance indicators of ED operations. Therefore, this study analyzes both the main and interaction effects of four widely-used independent determinants of ED-LOS. METHODS: The analysis was conducted using secondary data from an ED of a large urban hospital in Izmir, Turkey. Between-subject factorial analysis of variance (ANOVA) was used to test the main and interaction effects of the corresponding factors. P values <.05 were considered statistically significant. RESULTS: While the main effect of gender was insignificant, age, mode of arrival, and clinical acuity had significant effects, whereby ED-LOS was significantly higher for the elderly, those arriving by ambulance, and clinically-categorized high-acuity patients. Additionally, there was an interaction between the age and clinical acuity in that, while ED-LOS increased with age for high acuity patients, the opposite trend occurred for low acuity patients. When ED-LOS was modeled using gender, age, and mode of arrival, there was a significant interaction between age and mode of arrival. However, this interaction was not significant when the model included age, mode of arrival, and clinical acuity. CONCLUSION: Significant interactions exist between commonly used ED-LOS determinants. Therefore, interaction effects should be considered in analyzing and modelling ED-LOS.

Highlighting the rules between diagnosis types and laboratory diagnostic tests for patients of an emergency department: Use of association rule mining.

Diagnostic tests are widely used in emergency departments to make detailed investigations on diagnosis and treat patients correctly. However, since these tests are expensive and time-consuming, ordering correct tests for patients is crucial for efficient use of hospital resources. Thus, understanding the relation between diagnosis and diagnostic test requirement becomes an important issue in emergency departments. Association rule mining was used to extract hidden patterns and relation between diagnosis and diagnostic test requirement in real-life medical data received from an emergency department. Apriori was used as an association rule mining algorithm. Diagnosis was grouped into 21 categories based on International Classification of Disease, and laboratory tests were grouped into four main categories (hemogram, biochemistry, cardiac enzyme, urine and human excrement related). Both positive and negative rules were discovered. Since the nature of the data had the dominance of negative values, higher number of negative rules with higher confidences were discovered compared to positive ones. The extracted rules were validated by emergency department experts and practitioners. It was concluded that understanding the association between patient's diagnosis and diagnostic test requirement can improve decision-making and efficient use of resources in emergency departments. Association rules can also be used for supporting physicians to treat patients.

Sizing capacity levels in emergency medical services dispatch centers: Using the newsvendor approach.

BACKGROUND: The increased volume in demand worldwide in the present day has led to the need for the establishment of effective ambulance services. As call centers have become the primary contact point between patients and emergency service providers, the planning of the call center has become a key task for administrators. OBJECTIVES: The aim of this study is to apply a widely used operations management method, the newsvendor model, for optimizing the capacity level in EMS call centers with a minimum cost in order to efficiently meet the calls arriving. METHODS: Real-life data from a call center for ambulance services in a major city in Turkey was used. We propose using the newsvendor model for optimizing this call center's capacity level based on the forecasts of periodic call volumes via basic methods. RESULTS: Ambulance service call volumes vary during the day and weekday call profiles are different from weekends. By separating the analysis into weekdays and weekends and illustrating shorter time intervals within the days, call volume can be forecast. Taking not only the point forecast but also the variation of the forecast into account, the capacity level of each period can be planned in a cost-effective way. CONCLUSIONS: This paper provides a basis for operation planning strategies of ambulance services by reconsidering the uncertainties of demand. The newsvendor model, which works well under parameter uncertainty, can be used in planning the capacities of health care services, especially when high service levels are required.

An analysis of Emergency Medical Services demand: Time of day, day of the week, and location in the city.

OBJECTIVE: Effective planning of Emergency Medical Services (EMS), which is highly dependent on the analysis of past data trends, is important in reducing response time. Thus, we aimed to analyze demand for these services based on time and location trends to inform planning for an effective EMS. MATERIALS AND METHODS: Data for this retrospective study were obtained from the Izmir EMS 112 system. All calls reaching these services during first six months of 2013 were descriptively analyzed, based on time and location trends as a heat-map form. RESULTS: The analyses showed that demand for EMS varied within different time periods of day, and according to day of the week. For the night period, demand was higher at the weekend compared to weekdays, whereas for daytime hours, demand was higher during the week. For weekdays, a statistically significant relation was observed between the call distribution of morning and evening periods. It was also observed that the percentage of demand changed according to location. Among 30 locations, the five most frequent destinations for ambulances, which are also correlated with high population densities, accounted for 55.66% of the total. CONCLUSION: The results of this study shed valuable light on the areas of call center planning and optimal ambulance locations of Izmir, which can also be served as an archetype for other cities.

Child Maltreatment in Turkey: Comparison of Parent and Child Reports.

BACKGROUND AND AIM: Child maltreatment, i.e. abuse and neglect, is a significant problem worldwide and can cause impaired physical and mental health throughout life. The true extent still remains unknown in all countries, including Turkey. The aim of this study was to apply the two versions of the International Society for the Prevention of Child Abuse and Neglect (ISPCAN) Child Abuse Screening Tool of ICAST-C and ICAST-P, which are used to assess child and parent feedback and to compare reports given by children and those given by parents. This is the first study of its kind conducted in Turkey. METHODS: First, ICAST was translated into Turkish by bilingual experts. Students and their parents were asked to complete ICAST-C and ICAST-P respectively, with the help of trained researchers. In total, data from 2,608 matched reports (2,608 children and 2,608 parents) was obtained. Descriptive statistics were used to evaluate demographical variables, and chi-square tests were employed to investigate the statistical significance of comparisons. RESULTS: The present study demonstrated that Turkish parents consider rebukes, insults and corporal punishment effective ways of disciplining children. According to parents' reports, the use of psychological abuse was most prevalent against boys aged 16, while the use of physical abuse was most prevalent against boys aged 13. A statistically significant relationship was found between parents' economic conditions and child abuse (p<0.01). No significant relationship was detected between maternal educational levels and child abuse (p>0.05). However, the relationship between paternal educational background and psychological abuse was observed to be significant (p<0.05). A comparison of children's and parents' reports shows that parents tended to under-report child maltreatment. CONCLUSIONS: The results show that there is a significant healthcare problem in Turkey, since child maltreatment is prevalent, but parents are not generally aware of its extent. Possible approaches to changing this situation include efforts to increase education levels, promoting public awareness, and strengthening political commitments.

Child Abuse and Neglect Among Children Who Drop Out of School: A Study in Izmir, Turkey.

Child abuse and neglect (CAN), and dropping out of school have long been recognized as pervasive social problems globally, and Turkey is no exception. This study aims to explore the prevalence and incidence of CAN in children who drop out of school of Turkey, using the ISPCAN Child abuse Screening Tool, Children's Version, which is an appropriate tool for multinational comparisons. Data from a convenience sample of children who drop out of school age 11, 13, and 16 from Izmir were collected either by interviews or by self-completion. The results show that, compared to children who do not drop out of school, children who drop out of school have higher rates of psychological and physical abuse and neglect within the family. This study not only highlights the need for preventive laws for CAN and dropping out of school, but also points to direction for future research.